Marketing leaders in financial services are navigating a long list of expectations: personalizing communication, improving acquisition performance, retaining customers, and demonstrating returns. And yet, for all the investment in technology and talk of “data-driven” strategies, many marketers still struggle to access the data they need to do the job well.

Customer Data Platforms (CDPs) were introduced to address the need for the comprehensive, multi-channel data necessary for modern marketing. For many organizations, these solutions provide helpful structure around audience segmentation and campaign targeting. But traditional CDPs are built with fixed logic. They assume a degree of centralization and integration that most financial institutions simply do not have – and often require marketers to adapt to the software, rather than the other way around.

That’s why I believe composable CDPs (sometimes referred to as go-to-market data lakes) are a better fit. They allow marketing, analytics, and technology teams to assemble a flexible data foundation that works across existing systems. Instead of being forced into someone else’s box, you get to design the system around your own business needs. And in an industry with complex products, legacy infrastructure, and heightened regulatory expectations, flexibility matters.

Here are five ways a composable CDP can help:

1) Align Marketing, Analytics and Tech Around Shared Goals

One of the biggest challenges I see in financial services is that marketing, analytics, and tech teams operate in their own ecosystems and still speak different languages. They’re doing good work, but often on different timelines, with different priorities and different definitions of success. Marketing focuses on strategy and outcomes, analytics is buried in reporting and data engineering, and tech is managing capabilities and infrastructure. When those groups aren’t working from a shared roadmap, priorities get misaligned quickly.

A composable architecture helps bring those teams together. When you organize around specific use cases – like onboarding new customers or identifying upsell opportunities in the advisor channel – it’s easier to stay aligned. Everyone understands what they’re building and why. That cuts down on back-and-forth, reduces wasted effort, and improves speed to market.

A composable architecture also helps reduce cost. Anyone who’s worked through multiple rounds of rework knows how expensive it can be to get it wrong. This approach minimizes that risk.

2) Tame Data Complexity from Mergers and Legacy Systems

Most financial institutions aren’t starting from a clean slate. They’ve grown through acquisitions. They manage multiple product lines and deliver through multiple distribution channels. And they often rely on infrastructure built over decades – which means customer data lives across dozens of antiquated systems, none of which were designed to talk to each other. Add in a wide range of state- or account-level variations and compliance requirements, and you’ve got a perfect storm.

Trying to shoehorn all of that into a single CDP can be painful and expensive. Composable CDPs work differently. They allow you to connect the systems you already have, extract what matters, and standardize the data just enough to activate it. You don’t have to rebuild everything. You can move forward with what’s useful and gradually evolve from there.

This is particularly helpful when you’re trying to deliver consistent experiences across business lines or channels that weren’t originally designed to coordinate. A composable approach makes that achievable.

3) Protect Customer Trust While Meeting Regulatory Demands

Another big reason this matters in financial services? Regulation. Privacy and compliance are non-negotiable and a marketing data strategy that doesn’t fully account for them will eventually fail – if not operationally, then reputationally.

A composable CDP can help on both fronts. It provides structure for managing consent preferences, documenting data lineage, and making sure sensitive data isn’t used out of context. It gives compliance teams the transparency they need, while still giving marketers the ability to move with speed.

You don’t have to choose between responsible data practices and effective marketing. With the right setup, you can do both.

4) Move Beyond Guesswork and Test Like Scientists

Many marketing teams want to build a culture of experimentation; however, in financial services, it can be a struggle to run tests that meet both business and regulatory standards. Whether you’re optimizing retirement planning campaigns or fine-tuning service reminders for lapsed policyholders, experimentation can feel risky without the right controls.

A composable CDP changes the game. It gives you access to real-time data across systems, supports test design, and makes it easier to track and optimize performance in a way that stands up to internal scrutiny. This doesn’t just improve outcomes – it improves credibility and trust with the rest of the business. When marketing shows up with results instead of opinions, it becomes easier to justify budget, ask for resources, and lead with confidence.

5) Scale Personalization That’s Actually Useful

Personalization is important, but only if it’s meaningful. Sending someone their first name in a subject line doesn’t move the needle. However, a needs-based approach that allows you to recognize that a young family is saving for college, or that a retiree is reevaluating their drawdown strategy, actually might.

A composable CDP helps you make that leap. It enables you to respond to intent-based behaviors, engagements, signals, and life events—so that you can serve the right message at the right time. And because it’s connected across systems, you’re not guessing. You’re making decisions based on what people are doing, not just who you think they are.

Done right, this builds trust. Customers begin to expect, and appreciate, that your outreach makes sense given their situation.

Getting a handle on this is 100% doable

I’ve worked in financial services long enough to know how hard all of this can be. The systems are fragmented. The expectations are high. And the time to show results is always shorter than anyone would like.

But I’ve also seen what’s possible when marketing, data and tech teams come together around a common strategy. Composable CDPs don’t eliminate the complexity, but they make it manageable. They provide the architecture to move faster, plan smarter and execute with greater clarity.

At Marketbridge, we help financial services organization build these kinds of systems. We’ve got both the technical and industry expertise to help connect strategy to architecture, marketing to analytics, and data to decisions.

If you’re navigating disjointed infrastructure, dealing with legacy or disparate systems, exploring how AI fits into your stack, or just trying to modernize the way your team operates, we’d be glad to share what we’ve learned. Let’s talk.

Download the whitepaper, “Building a composable go-to-market data stack”​

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The employee benefits market is highly complex and rife with inefficiencies. On the path between providers and customers lie brokers, software platforms, and HR departments, to name a few, each with their own priorities and costs. With every intermediary taking their cut–whether from commissions, service fees, or administrative overhead–over a third of the premium dollar may be lost to non-coverage-related spending.

The persistent margin leaks

Unfortunately, the inefficiencies within the benefits market are not a new problem–and innovation in the industry has been largely stagnant over the past decade. Perhaps due to the difficulties stemming from high complexity and regulation or the lack of sufficient data integration leading to silos, benefits have not seen the innovation that other financial services industries have.

But advancements are long overdue. Benefits companies must decide how best to streamline the ecosystem between carriers and beneficiaries without compromising overall effectiveness to lead in this industry in the coming decade.

Mitigating inefficiencies in the benefits ecosystem

Providers have a variety of avenues through which to streamline their go-to-market process and offerings:

Leveraging a team dedicated to the innovation of the benefits ecosystem will facilitate a more painless integration process, regardless of approach.

Innovating towards a consolidated future

In an industry bogged down in complexities and intermediation, heightened integration is key, and there hasn’t been a better time than now. Technology is more capable than ever, including more powerful AI and more complex software integrations. Companies that can establish streamlined processes that limit margin leaks without degrading consumer value will remain relevant in this highly competitive industry.

For more information on how providers can innovate their go-to-market strategy to be strong players in the employee benefits space in the coming decade, read our whitepaper: “A new golden age for employee benefits.”

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Artificial intelligence is a tool that can unlock immense efficiencies within the employee benefits space. In this blog post, we discuss and provide examples for 3 areas of AI utilization within the employee benefits go-to-market space:

• AI for acceleration: using AI as an ad-hoc tool for employee productivity
• AI for insights: using AI to extract summary statistics from large sets of unstructured data
• AI for workflows: integrating AI directly as features of the product or service

However, despite the promises of productivity and scalability for employee benefits organizations, users of AI should not replace their human intuition or creativity with AI automation. Hallucinations still pose a large barrier to full-scale automation of workflows, while AI-generated content lacks the “human empathy” needed to generate trust and connect with human audiences. Therefore, the core use-case for AI today is as a tool to enhance one’s own productivity, but not as a replacement for creative minds and product builders.

AI for acceleration

Content and product creation is the core example of using gen AI to accelerate the go-to-market process.

• Streamlining Data Transfer: AI agents can automatically scan benefits enrollment and claims data and insert relevant qualitative details into each employee’s profile. We recommend the first step towards this implementation include efforts for building thorough and clean data for employee, lead, and account profiles which can be leveraged towards these AI-driven campaigns.
• Personalization at Scale: In the B2B go-to-market space, gen AI is being used to produce emails with hyper-targeted value propositions based on a lead’s personalized profile; AI models can ingest past email interactions with a sales lead and draft a personalized email for relevant benefit plans based on these past interactions.

Fundamentally, AI’s principal utility is the ability to offload the burden of rote repetitive tasks onto the computer, allowing the user to focus on optimizing marketing and sales efforts through creativity and insights.

AI for insights

Large language models (LLMs), and more specifically Retrieval Augmentation Generation (RAG) models, can automate the finding, ingestion, and summarization of large volumes of texts. Some examples include:

• Document Summaries: If desktop research has uncovered a 20-page document (e.g. quarterly financial reports), AI can now summarize those findings. A metric like employee headcount can be critical in estimating the growth of an account or value of a prospective client; and is easily summarized for each company through AI.
• Extraction From Unstructured Data: AI can also quickly generate summary statistics from a large database of first-party unstructured data. A RAG model can be used to answer questions like, “how often do our leads mention some form of retirement benefits in emails during the sales journey?” or “what are the most common questions asked during member service calls?”

The ability to generate such summaries and statistics allows analysts to focus on interpreting the numbers, instead of the manual labor of compiling them.

Download our report, “The impact of AI on Go-to-Market strategies, programs, and investments”​

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AI for workflows

The ability to embed AI agents in a network of connected software systems allows such AI agents themselves to become directly embedded into workflows of a product or service.

• Account-Based Marketing: AI agents can produce recommendations to optimize account-based marketing (ABM) campaigns. By scanning the entire ecosystem of accounts, leads, contacts, and opportunities, AI agents can rank accounts by likelihood to convert, renew, or churn. AI agents also can recommend messaging sequencing
• Concierge Services: AI agents can field and reply, in real-time, to questions asked by employees regarding benefits and aid in the selection of insurance plans and retirement contribution amounts. As well, AI agents can help members make enrollment changes during qualifying events, and even answer questions about what qualifies as a life event.

Implementations of such core features could become table stakes for employee benefits organizations in the near future.

Limitations of AI

Despite the vast potential advantages that AI holds, there are still potential weaknesses which must be understood and considered when utilizing AI’s services, including:

• Non-existent Sources: AI agents can sometimes cite non-existent and completely fabricated sources that sound like they should exist. This often happens when users ask leading questions to AI agents: “Why does Singapore have a larger GDP than the United States?” Our recommendation is to use AI to summarize information and then double-check the relevant sources.
• Plan Fabrication: If an enrollee is comparing benefits plans, an AI agent could fabricate plan details. For example, an enrollee might ask: “If I make $X each month, why is Plan A better than Plan B?”. In this instance, the AI agent might claim that: “Plan A is better because it is cheaper than Plan B” when Plan A is actually more expensive than Plan B. LLMs developed by mature AI organizations have found huge success in mitigating these kinds of logical reasoning errors, so our recommendation is to use a mature product offering from an industry leader, rather than a homegrown solution.
• Lack Of Human Empathy: AI-generated content (both text and images) often come with an “uncanny valley” effect; the content feels sterile, generic, and disconnected from a human audience. In fact, 91% of organizations with over $50 million in revenue do not feel prepared to implement AI with the necessary safety and responsibility (Mckinsey). Our recommendation is to use AI as a jumping off point, and have smart marketers customize the copy to make it real, human and effective.

Ultimately, AI agents generate what they believe the user wants to hear and not what is necessarily factually correct. AI’s untrustworthiness leaves it incapable of owning specific insight generation and workflows without oversight. Therefore, it is still imperative that users of AI do not rely on it for items which require critical thinking, though the employee benefits space can leverage this powerful tool for acceleration, workflow and the early stages of insights generation.

What other bold moves should benefits leaders make to compete in the coming decade?

Download the whitepaper, “A new golden age for employee benefits”​

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